Antarctic and Southern Ocean (ASO) marine ecosystems have been changing for at least the last 30 years, including in response to increasing ocean temperatures and changes in the extent and seasonality of sea ice; the magnitude and direction of these changes differ between regions around Antarctica that could see populations of the same species changing differently in different regions. This article reviews current and expected changes in ASO physical habitats in response to climate change. It then reviews how these changes may impact the autecology of marine biota of this polar region: microbes, zooplankton, salps, Antarctic krill, fish, cephalopods, marine mammals, seabirds, and benthos. The general prognosis for ASO marine habitats is for an overall warming and freshening, strengthening of westerly winds, with a potential pole-ward movement of those winds and the frontal systems, and an increase in ocean eddy activity. Many habitat parameters will have regionally specific changes, particularly relating to sea ice characteristics and seasonal dynamics. Lower trophic levels are expected to move south as the ocean conditions in which they are currently found move pole-ward. For Antarctic krill and finfish, the latitudinal breadth of their range will depend on their tolerance of warming oceans and changes to productivity. Ocean acidification is a concern not only for calcifying organisms but also for crustaceans such as Antarctic krill; it is also likely to be the most important change in benthic habitats over the coming century. For marine mammals and birds, the expected changes primarily relate to their flexibility in moving to alternative locations for food and the energetic cost of longer or more complex foraging trips for those that are bound to breeding colonies. Few species are sufficiently well studied to make comprehensive species-specific vulnerability assessments possible. Priorities for future work are discussed.

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openAccessArticle: FalsePage Range: 74-74doi: 10.1016/j.pocean.2012.03.006Harvest Date: 2016-01-12 15:13:57issueName: End-to-End Modeling: Toward Comparative Analysis of Marine Ecosystem Organizationcover date: 2012-09-01pubType

Globally, collapse of ecosystems—potentially irreversible change to ecosystem structure, composition and function—imperils biodiversity, human health and well‐being. We examine the current state and recent trajectories of 19 ecosystems, spanning 58° of latitude across 7.7 M km2, from Australia's coral reefs to terrestrial Antarctica. Pressures from global climate change and regional human impacts, occurring as chronic ‘presses’ and/or acute ‘pulses’, drive ecosystem collapse. Ecosystem responses to 5–17 pressures were categorised as four collapse profiles—abrupt, smooth, stepped and fluctuating. The manifestation of widespread ecosystem collapse is a stark warning of the necessity to take action. We present a three‐step assessment and management framework (3As Pathway Awareness, Anticipation and Action) to aid strategic and effective mitigation to alleviate further degradation to help secure our future.

Qualitative network analyses provide a broad range of advantages for formulating ideas and testing understanding of ecosystem function, for exploring feedback dynamics, and for making qualitative predictions in cases where data are limited. They have been applied to a wide range of ecological questions, including exploration of the implications of uncertainty about fundamental system structure. However, we argue that questions regarding model uncertainty in qualitative network analyses have been under‐explored, and that there is a need for a coherent framework for evaluating uncertainty. To address this issue, we have developed a Bayesian framework for interpreting uncertainty that can be applied when comparing and evaluating the characteristics and behavior of alternative model formulations. Specifically, we recognize that results from previously developed simulation approaches to qualitative modeling can be interpreted as marginal likelihoods that translate to Bayes factors for model comparison. We then test and extend our Bayesian interpretation of qualitative model results to address comparisons both between and within alternative models. With the use of examples, we demonstrate how our Bayesian framework for interpretation can improve the application of qualitative modeling for addressing uncertainty about the structure and function of ecological networks.

The ecosystem approach to fisheries recognises the interdependence between harvested species and other ecosystem components. It aims to account for the propagation of the effects of harvesting through the food-web. The formulation and evaluation of ecosystem-based management strategies requires reliable models of ecosystem dynamics to predict these effects. The krill-based system in the Southern Models of the Southern Ocean ecosystem. 2Ocean was the focus of some of the earliest models exploring such effects. It is also a suitable example for the development of models to support the ecosystem approach to fisheries because it has a relatively simple food-web structure and progress has been made in developing models of the key species and interactions, some of which has been motivated by the need to develop ecosystem-based management. Antarctic krill, Euphausia superba, is the main target species for the fishery and the main prey of many top predators. It is therefore critical to capture the processes affecting the dynamics and distribution of krill in ecosystem dynamics models. These processes include environmental influences on recruitment and the spatially variable influence of advection. Models must also capture the interactions between krill and its consumers, which are mediated by the spatial structure of the environment. Various models have explored predator-prey population dynamics with simplistic representations of these interactions, while others have focused on specific details of the interactions. There is now a pressing need to develop plausible and practical models of ecosystem dynamics that link processes occurring at these different scales.Many studies have highlighted uncertainties in our understanding of the system, which indicates future priorities in terms of both data collection and developing methods to evaluate the effects of these uncertainties on model predictions. We propose a modelling approach that focuses on harvested species and their monitored consumers and that evaluates model uncertainty by using alternative structures and functional forms in a Monte Carlo framework.